Only 14 pages are availabe for public view
The area of oil and gas production may have more noteworthy radioactivity levels, due to the disposal petroleum pipes containing sludge and scale as a technologically enhanced natural occurring radioactive material (TENORM) which lead to potential radiological and health dangers. The specific radioactivity concentrations of 226Ra, 232Th and 40K in 124 of soil and waste petroleum samples such as sludge (from waste pit) and scale contained in the disposal petroleum pipes in the area of oil and gas production of the Thi-Qar province, southeast of Iraq were measured using the gamma-ray spectrometer.
The measured samples were collected from six different locations. The average values of the specific concentrations of 226Ra, 232Th and 40K of all samples were less than the release level recommended by the IAEA. The highest radioactivity concentrations of 226Ra, 232Th and 40K among the different investigated samples were 19.90 Bq/kg (sludge), 20.06 Bq/kg (Soil) and 480.33 Bq/kg (soil) respectively. The obtained results have been compared with the previous works in different regions in Iraq and the world average values specified by the UNSCEAR reports. The radiological indices such as radium equivalent, external hazard index, radiation dose rate and effective dose were determined, and their values were lower than the recommended regulatory limits. Thus, this study confirmed that exposure to the natural radiation coming from the soil or any petroleum materials in oil and gas production area of the Thi-Qar province did not present any signiﬁcant radiological hazard to the public and the workers.
One of the main issues facing the standard measurement procedure is determining the appropriate volume of the sample to be taken from the area under investigation. The volume of the investigated sample affects the detection efficiency of the HPGe detector due to the self-shielding effect. the artificial neural networks can help in choosing the proper dimensions of the Marinelli beaker to be used in the detection process. Various soil sample compositions can be used as a case study. The obtained results showed that, the artificial neural networks could find the relation between the soil composition, Marinelli beaker dimensions, and the gamma energy as inputs and the HPGe detection efficiency as output. The developed model’s coefficient of determination was 99% compared to the detection efficiency calculated using MCNP.